Recent innovations in model fitting technology allow ecologists to fit an impressive array of complex, hierarchical and parameter-rich models to data. Bayesians and frequentists alike can harness modern computational power to entertain and fit models of complexity that was out of practical reach less than a generation ago. With this model fitting capability in hand, a necessary next question is: how does one choose the right level of model complexity for a given problem? This session brings together ecologists and applied statisticians who have grappled recently with questions about model complexity or parsimony in an ecological context. Model complexity and parsimony is a particularly interesting axis on which to frame a discussion of contemporary statistical frontiers because it is an issue confronted by all data analysts, and it impacts the interpretation of scientific results in major ways. However, different statistical paradigms generate different perspectives concerning how model complexity affects the interpretation of an analysis and how one interprets model parsimony. This session includes speakers from a variety of statistical paradigms and asks each to emphasize how their different vantage points may lead to different viewpoints on parsimony in statistical modeling. The session will also emphasize practical strategies for data-driven model selection with real data analysis problems. Speakers will illustrate viewpoints on model complexity in the context of real ecological data analysis problems. We hope that this session will interest a practical audience, and provide a useful roadmap of the different philosophies and methodologies regarding model complexity and model selection.